All datasets are open access. You can freely download and use the data.
If there is an associated publication, please make sure to cite it.

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MI

fNIRS Dataset for Finger/Foot Motor Execution Task

Participants        30
Signals              20-channel fNIRS
Licensor             Korea University
Description         PDF
Citation              It will be updated soon. Please contact admin.

EEG Dataset for 2-Class MI

Participants        12
Signals              30-channel EEG
Licensor             Korea University
Description         PDF
Citation              K.-T. Kim, H.-I. Suk, and S.-W. Lee, “Commanding a Brain-Controlled Wheelchair using Steady-State Somatosensory Evoked Potentials,” IEEE Trans. on Neural Systems & Rehabilitation Engineering, Vol. 25, 2017. (Accepted)

EEG Dataset for MI-based BCI

Participants        52
Signals              64-channel EEG, 2-channel EMG
Licensor             Gwangju Institute of Science and Technology
Description         PDF
Citation              H. Cho, M. Ahn, S. Ahn, K. Kwon, and S. C. Jun, “EEG Datasets for Motor Imagery Brain-Computer Interface,” GigaScience, Vol. 6, No. 1, 2017, pp. 1-8.

EEG Dataset during Conventional MI

Participants        52
Signals              70-channel EEG, 6-channel EMG, 1-channel EOG
Licensor             Korea University
Description         PDF
Citation              M.-H. Lee, K.-T. Kim, Y.-J. Kee, J.-H. Jeong, S.-M. Kim, S. Fazli, and S.-W. Lee, “OpenBMI: A real-time data analysis toolbox for Brain-Machine Interfaces,” Proc. IEEE International Conference on Systems, Man and Cybernetics, Budapest, Hungary, Oct. 9-12, 2016.

MEG/EEG Dataset for MI-BCI

Participants        10
Signals              19-channel EEG, 150-channel MEG
Licensor             Gwangju Institute of Science and Technology
Description         PDF
Citation              M. Ahn, S. Ahn, J. H. Hong, H. Cho, K. Kim, B. S. Kim, J. W. Chang, and S. C. Jun, “Gamma Band Activity Associated with BCI Performance: Simultaneous MEG/EEG Study,” Frontiers in Human Neuroscience, Vol. 7, 2013, article 848.

SSVEP

A Multi-Day and Multi-Band EEG Dataset for Steady-State Visual Evoked Potential

Participants        30
Signals              33-channel EEG
Licensor             Kumoh National Institute of Technology
Description         PDF
Citation              G.-Y. Choi, C.-H. Han, Y.-J. Jung, H.-J. Hwang, “A multi-day and multi-band dataset for steady-state visual evoked potential-based brain-computer interface”, Gigascience, 2019. (Accepted)

SSVEP BCI Game in Real – Exhibition Environment

Participants        71
Signals              19-channel ear-EEG
Licensor             Gwangju Institute of Science and Technology
Description         PDF
Citation              It will be updated soon. Please contact the admin.

EEG Dataset for SSVEP using Ear-EEG and Scalp-EEG

Participants        11
Signals              18-channel ear-EEG, 8-channel scalp-EEG
Licensor             Korea University
Description         PDF
Citation              N.-S. Kwak and S.-W. Lee, “Error Correction Regression Framework for Enhancing the Decoding Accuracies of Ear-EEG Brain-Computer Interfaces,” IEEE Trans. on Cybernetics, 2019. (Accepted)

EEG Dataset for SSVEP under Ambulatory Environment

Participants        7
Signals              8-channel EEG
Licensor             Korea University
Description         PDF
Citation              N.-S. Kwak, K. Muller, and S.-W. Lee, “A Convolutional Neural Network for Steady State Visual Evoked Potential Classification under Ambulatory Environment,” PLOS ONE, Vol. 12, No. 2, 2017, article 0172578.

EEG Dataset for High-Frequency SSVEP based Speller

Participants        26
Signals              32-channel EEG
Licensor             Korea University
Description         PDF
Citation              D.-O. Won, H.-J. Hwang, S. Daehne, K.-R. Muller, and S.-W. Lee, “Effect of Higher Frequency on the Classification of Steady State Visual Evoked Potentials,” Journal of Neural Engineering, Vol. 13, No. 1, 2015, pp. 1-11.

ERP

EEG Dataset for ERP-based Random Speller

Participants        20
Signals              24-channel EEG
Licensor             Korea University
Description         PDF
Citation              M.-H. Lee, K.-T. Kim, Y.-J. Kee, J.-H. Jeong, S.-M. Kim, S. Fazli, and S.-W. Lee, “OpenBMI: A real-time data analysis toolbox for Brain-Machine Interface,” Proc. IEEE International Conference on Systems, Man and Cybernetics, Budapest, Hungary, Oct. 9-12, 2016.

EEG Dataset for ERP during Simulated Driving

Participants        15
Signals              64-channel EEG, 1-channel EMG
Licensor             Korea University
Description         PDF
Citation              I.-H. Kim, J.-W. Kim, S. Haufe, and S.-W. Lee, “Detection of Braking Intention in Diverse Situations during Simulated Driving based on EEG Feature Combination,” Journal of Neural Engineering, Vol. 12, No. 1, 2015, pp. 1-12.

Cognitive Task

EEG Dataset Induced by Watching Emotional Clips

Participants        18
Signals              14-channel EEG
Licensor             Yonsei University
Description         PDF
Citation             It will be updated soon. Please contact admin.

EEG Data Acquired from Lie Detection Experimental Paradigm

Participants        24
Signals              28-channel EEG
Licensor             Korea University
Description         PDF
Citation             It will be updated soon. Please contact admin.

Ear-EEG Dataset During Mental Arithmetic

Participants        18
Signals              25-channel scalp-EEG, 9-channel Ear-EEG
Licensor             Kumoh National Institute of Technology
Description         PDF
Citation             S.-I. Choi, C.-H. Han, G.-Y. Choi, J. Y. Shin, K. S. Song, C.-H. Im, and H.-J. Hwang, ” On the Feasibility of Using Ear-EEG to Develop an Endogenous Brain-Computer Interface”, Sensors, Vol. 18, No. 9, 2018, article 2856.

EEG/NIRS Dataset During Mental Arithmetic and Word Chain

Participants        12
Signals              22-channel EEG, 9-channel NIRS
Licensor             Kumoh National Institute of Technology and Technical University of Berlin
Description         PDF
Citation              J. Y. Shin, D.-W. Kim, K.-R. Müller, and H.-J. Hwang, “Improvement of Information Transfer Rate by Hybrid EEG-NIRS Brain-Computer Interface with Short Task Duration: Offline and Pseudo-Online Analyses”, Sensors, Vol. 18, No. 6, 2018, article 1827.

EEG Data Acquired from Risk Taking Balloon Task (BART)

Participants        55
Signals              32-channel EEG
Licensor             Korea University
Description         PDF
Citation              It will be updated soon. Please contact the admin.

EEG Resting State in Real World – Exhibition Environment

Participants        44
Signals              19-channel EEG
Licensor             Gwangju Institute of Science and Technology
Description         PDF
Citation              It will be updated soon. Please contact the admin.

Emotional EEG/ECG/Face Dataset using Movie Clip Stimuli

Participants        10
Signals              14-channel EEG
Licensor             Yonsei University
Description         PDF
Citation              It will be updated soon. Please contact the admin.

EEG Dataset for Brainwave Entrainment using Auditory Stimulation

Participants        10
Signals              19-channel EEG
Licensor             Korea University
Description         It will be updated soon.
Citation              It will be updated soon. Please contact the admin.

EEG Dataset Pre and Post SW/HW Neurofeedback

Participants        30 (SW: 15, HW: 15)
Signals              29-channel EEG (SW), 9-channel EEG (HW)
Licensor             Korea University
Description         It will be updated soon.
Citation              It will be updated soon. Please contact the admin.

EEG Dataset during German Vocabulary Learning Task

Participants        14
Signals              63-channel EEG
Licensor             Korea University
Description         PDF
Citation              It will be updated soon. Please contact the admin.

EEG/ECG Dataset for Emotion Task

Participants        80 (10 groups – 8 participants per group)
Signals              8-channel EEG, 2-channel ECG
Licensor             Gwangju Institute of Science and Technology
Description         PDF
Citation              It will be updated soon. Please contact the admin.

EEG/NIRS Dataset during Mental Arithmetic

Participants        12
Signals              22-channel EEG, 9-channel NIRS
Licensor             Kumoh National Institute of Technology and Technical University of Berlin
Description         PDF
Citation              J. Y. Shin, K.-R. Muller, and H.-J. Hwang, “Eyes-closed Hybrid Brain-Computer Interface Employing Frontal Brain Activation,” PLOS ONE, Vol. 13, No. 5,  2018, article 0196359.

EEG/NIRS Dataset during Cognitive Tasks

Participants        26
Signals              30-channel EEG, 36-channel NIRS
Licensor             Technical University of Berlin and Kumoh National Institute of Technology
Description         PDF
Citation              J. Y. Shin, V. L. Alexander, D.-W. Kim, M. Jan, H.-J. Hwang, and K.-R. Muller, “Simultaneous Acquisition of EEG and NIRS during Cognitive Tasks for an Open Access Dataset,” Scientific Data, Vol. 5, 2018, article 180003.

MEG/EEG Dataset for Verbal-Interaction Hyperscanning Task

Participants        10 (5 pairs)
Signals              19-channel EEG, 152-channel MEG
Licensor             Gwangju Institute of Science and Technology
Description         PDF
Citation              S. Ahn, H. Cho, M. Kwon, K. Kim, H. Kwon, B. S. Kim, W. S. Chang, J. W. Chang, and S. C. Jun, “Interbrain Phase Synchronization during Tum-Taking Verbal Interaction–A Hyperscanning Study using Simultaneous EEG/MEG,” Human Brain Mapping, 2017. (Accepted)

EEG/ECG/EOG/fNIRS Dataset for Drowsy Driving Task

Participants        11
Signals              64-channel EEG, 2-channel ECG, 2-Channel EOG, fNIRS (2-LED, 8-Detector)
Licensor             Gwangju Institute of Science and Technology
Description         PDF
Citation              S. Ahn, T. Nguyen, H. Jang, J. G. Kim, and S. C. Jun, “Exploring Neurophysiological Correlates of Drivers’ Mental Fatigue caused by Sleep Deprivation using Simultaneous EEG, ECG, and fNIRS Data,” Frontiers in Human Neuroscience, Vol. 10, 2016, article 219.

EEG Dataset for Two-Stage Markov Decision Task

Participants        18
Signals              64-channel EEG
Licensor             Korea Advanced Institute of Science and Technology
Description         PDF
Citation              S. W. Lee, S. Shimojo, and J.P. O’Doherty, “Neural Computations Underlying Arbitration between Model-Based and Model-free Learning,” Neuron, Vol. 81, No. 3, 2014, pp. 687-699.